Fractals show machine intentions

By
Eric Smalley,
Technology Research NewsThere has been much research and musing
about how autonomous machines like robots and intelligent software agents
should interact with people. Much of the work focuses on giving machines
a degree of social intelligence that will allow people to understand and
communicate with them on human terms.

A sense of internal states is integral to human communications:
it's useful to have a sense of when a human is annoyed. In contrast, it's
often impossible to determine whether a robot is processing data, awaiting
instruction or in need of repair.

Researchers from Switzerland and South Africa have designed a
visual interface that would give autonomous machines the equivalent of
body language.

The interface represents a machine's internal state in a way that
makes it possible for observers to interpret the machine's behavior. "Our
idea of communication has a strong focus on learning and interpretation
-- trying to create relationships between the internal machine variables
and the macroscopic behavior," said Jan-Jan van der Vyver, a researcher
at the University of Zürich and the Swiss Federal Institute of Technology.

The researchers' autonomous machine interface consists of a clustering
algorithm that groups the machine's many internal states into a manageable
number of representations, and a fractal generator.

Clustering algorithms organize data like that contained in genes
into groups with similar traits, and analyze raw data without any sense
of the data's meaning or assumptions about how it should be structured.

In the researchers' scheme, snapshots of a machine's sensory input,
computational processing and output are clustered and the clusters are
displayed as fractal images. The fractal generator produces a fractal
pattern in the center of the display and patterns move outward in concentric
rings, giving observers a sense of change over time.

Fractal generators produce a large variety patterns that people
are quick to distinguish. A set of snapshots corresponding to a high degree
of sensory stimulation could be clustered into a representation of the
machine that people learn to associate with the machine observing a change
in its environment, for example.

In coming up with a way to convey the data, the researchers were
careful to avoid any anthropomorphic representations that human observers
might associate with particular behaviors or intentions, according to
van der Vyver. Those associations are not likely to correspond to the
machine's behavior, he said.

The fractal display served as the interface to a neural network
that controlled the input and output devices of a smart room at the Swiss
national exposition Expo.02 from May to October 2002. Exposition goers
were able to interact with the room through the room's cameras, microphones,
pressure sensors, light projectors and speakers.

Observers were able to correlate the room's behavior with the
fractal display, said van der Vyver. "What we found surprising was that
the general public so quickly gravitated toward our chosen implementation
of the communication interface, and so quickly learned to interpret it,"
he said.

The smart room, dubbed Ada -- The Intelligent Space, was not a
fully autonomous system, but demonstrated the viability of the fractal
display, said van der Vyver. Truly autonomous systems are likely to emerge
in the future, in part due to self-developing technologies like genetic
algorithms that evolve optimized designs, he said.

Given the prospect of self-evolving machines, the researchers
argue for a broad definition of autonomous systems as systems developing
according to their own dynamics through interaction with their environment.
The ultimate in autonomous machines is a system that develops intelligent
behavior simply as a result of participating in a society, van der Vyver
said.

It's not clear that the researchers' approach is necessary, said
Jeffrey Nickerson, an associate professor of computer science at Stevens
Institute of Technology. Autonomous machines could be programmed to explicitly
represent their intentions, he said. "If understanding intentions is hard,
then why not force the machine to provide indications of intentions, or
at least a trace of reasoning?"

Initial practical applications of the researchers' work are about
five years away, said van der Vyver. "As the development and deployment
of more autonomous machines takes place, this research comes into play,"
he said. However, self-evolving, self-repairing machines are a long way
off, he said.

Van der Vyver's research colleagues were Markus Christen and Thomas
Ott of the University of Zürich and the Swiss Federal Institute of Technology,
Norbert Stoop of the Swiss Federal Institute of Technology, Willi-Hans
Steeb of the Rand Afrikaans University, the International School for Scientific
Computing in South Africa and the University of Applied Sciences of Northwestern
Switzerland, and Ruedi Stoop of the Swiss Federal Institute of Technology
and the University of Applied Sciences of Northwestern Switzerland. The
work appeared in the March 31, 2004 issue of Robotics and Autonomous
Systems. The research was funded by the researchers' institutions.